Image Classification and Segmentation

Part 1: Image Classification

Val Accuracy of the network: 95 %

Test Accuracy of the network: 90 %

Accuracy of T-shirt/top : 84 %

Accuracy of Trouser : 97 %

Accuracy of Pullover : 89 %

Accuracy of Dress : 92 %

Accuracy of Coat : 88 %

Accuracy of Sandal : 97 %

Accuracy of Shirt : 67 %

Accuracy of Sneaker : 97 %

Accuracy of Bag : 97 %

Accuracy of Ankle boot : 95 %

Part 2: Semantic Segmentation

Network architecture

I used a deep convolutional neural net with 6 layers. The first 3 convolutional layers grow the number of channels from 48 to 128 to 192. After each convolution, the image shrinks by a factor of 2. There's also nonlinearities and a batch norm. Then, the last 3 layers are upscaling the network. The ConvTranspose2d upsamples the image by a factor of 2, while keeping the number of channels constant. Then we shrink the channels to eventually get to 5, which is our desired output.

Class	Average percision on test set
others	0.6794454505246241
facade	0.7672731673796042
pillar	0.15840029845440098
window	0.847716567883514
balcony	0.49730511712925923

Results